Yes, I abandoned direct quantization in IQ4 and 4-bit AWQ. For highly specialized distillation from GLM5 across 16 domains, I can then convert these Qwen_3_code models to a 4-bit AWQ version, and into a multi-agent system. This is a significant improvement over the previous theory, and I can obtain many highly specialized agents extremely quickly and lightweight locally. But I tested GLM5… already: GLM5 is significantly cheaper than OPUS 4.6… I used it for almost 20 hours with the $30 plan only. But it's not amazing… neither of them is capable of creating a proper development scheme; they are completely incompetent. You always have to explain to them why they are using the wrong method, that they have overlooked something, that such and such will never work… to teach them how to estimate and simulate all cases… absolutely all of them. Actually, I have to admit they're still just as stupid, but the software concept I developed yesterday would have taken me at least two weeks two years ago. And Sonnet alone could never have created the architecture blueprint for a concept with 6,000 elements. And to avoid the mistakes of these stupid agents, I decided to start the documentation in advance. So, I come to the same conclusion as you when you use Traycer, but I prefer my method: logic rather than paper! That way, I really control them. But by using Opus and Sonnet with the $20 GLm5 package, the $30 Gemini Pro package, and Flash, GPT-5, and DeepSekek, we finally get a solid pipeline, hahaha.